Knowledge catalogue
🆕Biohub’s Protein World Model: ESMC-6B, ESMFold2, 6.8B proteins, 1.1B structures, antibody design, SAEs, & the bitter lesson for biology https://www.latent.space/p/esmfold2 @biohub Head of Science @al
arXiv:2605.26820v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a promising foundation for general-purpose robotics. However, their successful deployment in real-world scen
arXiv:2605.27281v1 Announce Type: new Abstract: Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call
arXiv:2605.27146v1 Announce Type: new Abstract: Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate the reliance on large, annotated datasets, a common bottleneck in medical
arXiv:2601.22519v2 Announce Type: replace-cross Abstract: Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alter
arXiv:2405.16417v2 Announce Type: replace Abstract: Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often invo
arXiv:2603.16870v2 Announce Type: replace-cross Abstract: Recent advances in video generation have revealed an unexpected phenomenon: diffusion-based video models exhibit non-trivial reasoning capabil
arXiv:2601.20796v2 Announce Type: replace Abstract: Transformer-based multimodal large language models often exhibit in-context learning (ICL) abilities. Motivated by this phenomenon, we ask: how do t
arXiv:2605.26680v1 Announce Type: cross Abstract: Recent video multimodal large language models (MLLMs) increasingly couple step-by-step reasoning with on-demand visual evidence retrieval, allowing mo
AI adoption has become a culture shift for enterprises, moving beyond technical implementation to fundamentally reordering how organizations work, compete and grow. Few partnerships are better positio
arXiv:2605.26823v1 Announce Type: new Abstract: Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic
arXiv:2603.09551v2 Announce Type: replace Abstract: While Vision-Language Models (VLMs) have significantly advanced remote sensing interpretation, enabling them to perform complex, step-by-step reason
arXiv:2504.08593v5 Announce Type: replace-cross Abstract: This work tackles the challenge of continuous sign language segmentation, a key task with huge implications for sign language translation and
arXiv:2605.27310v1 Announce Type: new Abstract: Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they often reason in language and lose the fine-grained geometry nee
In this post, we share how we built NarrateAI using Amazon Bedrock AgentCore to deliver business intelligence at scale for the AWS SMGS (Sales, Marketing and Global Services) organization. You will le
arXiv:2512.17090v2 Announce Type: replace-cross Abstract: Squared tensor networks (TNs) and their extension as computational graphs--squared circuits--have been used as expressive distribution estimat
The N.E.A.T (NeuroEvolution of Augmenting Topologies) algorithm is an evolutionary machine learning approach that evolves neural networks to solve control problems. This post describes applying N.E.A.
arXiv:2605.26914v1 Announce Type: new Abstract: We present an image-conditioned point cloud completion approach that treats images as the primary geometric source rather than a secondary guide. To thi
Identifiability is what it means to learn the right latent space, the Gaussian is why LeJEPA works, and it's what makes planning transfer. I think identifiability is the right definition of what it me
arXiv:2605.26356v1 Announce Type: new Abstract: In-context learning has recently been linked to implicit gradient descent in linear self-attention models, suggesting that context can induce a forward-
Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation http://pub.sakana.ai/diffusionblocks What if we didn’t have to hold an entire neural network in memory to t
It is refreshing to have some new arguments pointing at specific distributions (Gaussian) our embeddings must follow! Especially now that we know how to do scalable distribution matching in high dimen
Boston Tech Week featured a session hosted by Mason Dry and Sydney Runkle with the Blitzy AI team focused on building deep agents. The event was promoted via Luma Events and appears to be an education
arXiv:2605.26725v1 Announce Type: new Abstract: Accurate interpretation of street-level imagery is essential for large-scale urban mapping and the creation of Spatial Digital Twin (SDT) environments.
arXiv:2605.26850v1 Announce Type: new Abstract: Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matc
arXiv:2601.03079v4 Announce Type: replace Abstract: Moral sensitivity is the most fundamental capability underlying human moral competence. Although many approaches aim to align large language models
arXiv:2605.26933v1 Announce Type: new Abstract: Unsupervised visual object tracking is a challenging task that requires following arbitrary targets in videos without training on ground-truth annotatio
arXiv:2506.11253v2 Announce Type: replace Abstract: Machine unlearning removes certain training data points and their influence from AI models (e.g., when a data owner revokes their consent to allow m
arXiv:2605.26741v1 Announce Type: cross Abstract: Inverse design of materials has significantly advanced target-driven formulation optimization, yet existing materials machine learning benchmarks rema
arXiv:2605.26454v1 Announce Type: new Abstract: Large language models (LLMs) learn undesirable properties during pretraining, including dangerous knowledge and toxic text generation. Just as post-trai
arXiv:2605.27306v1 Announce Type: new Abstract: We consider training classifiers for 3D medical images using only one binary label for the entire volume rather than a label for each 2D slice. In such
arXiv:2605.26649v1 Announce Type: new Abstract: RGB-based imitation learning requires many demonstrations to generalize to unseen objects or scenes, motivating research into intermediate representatio
arXiv:2605.26840v1 Announce Type: new Abstract: Reinforcement learning with evaluation metrics as rewards is widely used to enhance specific capabilities of language models. However, for tasks such as
arXiv:2605.27022v1 Announce Type: new Abstract: Causal analysis is a crucial task in many domains, including manufacturing, social science, and medicine. However, despite recent progress, the conceptu
arXiv:2605.26353v1 Announce Type: cross Abstract: Different visual patterns appear with different frequencies in the world: e.g., beach balls appear on sand more often than they do on a road. These st
arXiv:2605.27128v1 Announce Type: new Abstract: Real-time semantic segmentation models offer an excellent balance between accuracy and inference speed. However, deploying these models in dynamic real
arXiv:2605.26167v1 Announce Type: cross Abstract: We propose Lie group embedded dynamical neural networks (LieEDNN) and the corresponding learning algorithms based on gradient descent and metric proje
arXiv:2605.26703v1 Announce Type: cross Abstract: The classic concept of 'calibrated forecasts' and its more recent refinement, 'calibeating,' are defined with respect to the standard quadratic scorin
arXiv:2605.26819v1 Announce Type: cross Abstract: We present RAGEAR (Retrieval-Augmented Graph-Enhanced Academic Recommender), a neurosymbolic recommender system for academic course recommendation. RA
arXiv:2605.27080v1 Announce Type: new Abstract: Appearance-based gaze estimation always suffers from poor generalization due to limited annotated samples and insufficient dataset diversity. Leading ap
arXiv:2605.27030v1 Announce Type: new Abstract: Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution
arXiv:2605.26894v1 Announce Type: new Abstract: In point clouds, noise directly perturbs point coordinates that encode both spatial location and geometry, making one-to-one correspondence construction
The entire event yesterday, launching missions, landers, rovers, tech demos building a Moon Base, and sci-fi capabilities like MoonFall, brought to you by those who Dare Mighty Things. Just the very b
The fact that tokens went from something no one even put in a budget line a year ago to an absolute requirement for coding now is the cause of handwringing, not that AI is not turning out to be useful
This Ebola outbreak has been a slow motion disaster - 900 cases and and now threatening the U.S.. We know how to stop outbreaks like this. But Trump chose not to stop it. He destroyed our global healt
arXiv:2605.26405v1 Announce Type: new Abstract: Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback
arXiv:2605.26713v1 Announce Type: cross Abstract: Prior-data fitted networks (PFNs) have recently emerged as a powerful approach for Bayesian prediction tasks, approximating the posterior predictive d
arXiv:2511.17852v2 Announce Type: replace Abstract: Transformers can acquire Chain-of-Thought (CoT) capabilities to solve complex reasoning tasks through fine-tuning. Reinforcement learning (RL) and s
We're on a dangerous trajectory toward superintelligent AI that nobody controls. ControlAI's Adam Shimi (@epist_vigilance) and Connor Leahy (@NPCollapse) lay out the problem and how to tackle it, in a
What does JEPA actually learn? We can finally prove it 🌍 So excited to share our theory of identifiable World Models: LeJEPA recovers the latent variables of the world. Plan in the learned World Model
Jeremy Howard comments on the Xiaomi MiMo v2.5 model, highlighting its exceptional value proposition and cost-effectiveness for prompt processing. The post appears to include comparative pricing data
arXiv:2605.25391v1 Announce Type: new Abstract: We study the restless contextual multi-play multi-armed bandit (MP-MAB) problem for channel allocation in the opportunity spectrum access (OSA) scenario
arXiv:2605.25011v1 Announce Type: new Abstract: We consider the challenge of developing agents that efficiently interact with high-dimensional, evolving environments, towards a view of practical reinf
arXiv:2605.24298v1 Announce Type: cross Abstract: The growing use of Large Language Models (LLMs) for automated code generation has enhanced software development efficiency, but often at the cost of s
arXiv:2605.24457v1 Announce Type: cross Abstract: Data streams in real-world industrial scenarios often contain transitional operating conditions that are uncovered during offline training, leading to
arXiv:2605.23937v1 Announce Type: new Abstract: Knowledge base (KB) embeddings aim at combining the capability of classical knowledge graph embeddings to generalize the information present in facts, t
arXiv:2605.24632v1 Announce Type: cross Abstract: Recent demonstrations of large language models producing candidate and confirmed vulnerabilities in production software have renewed the narrative tha
arXiv:2508.11872v3 Announce Type: replace-cross Abstract: Traditional syllabi often function as static reference documents rather than engaging introductions to a course. In practical teaching, we obs
everyone is talking about self-optimizing loops in software & agents. but what does that actually mean? in my mind, it's a system that observes it's own outputs, evaluates them, and uses that signal t
arXiv:2605.25477v1 Announce Type: cross Abstract: The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demons